COVID-19-Predictions.ipynb

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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>154999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>156513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>157770</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          day   cases\n",
       "69 2020-04-24  154999\n",
       "70 2020-04-25  156513\n",
       "71 2020-04-26  157770"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "\n",
    "data = pd.read_csv(\"corona_infections.csv\", header=0, names=[\"day\", \"cases\"])\n",
    "data[\"day\"] = [dt.datetime.strptime(d + ' 2020', \"%b %d %Y\") for d in data[\"day\"]]\n",
    "data.tail(3)"
   ]
  },
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       "alt.Chart(...)"
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   ],
   "source": [
    "infections = alt.Chart(data).mark_line(point=True).encode(\n",
    "    alt.X(\"monthdate(day):O\", title=\"Tag\"),\n",
    "    alt.Y(\"cases:Q\", title=\"Corona-Fallzahlen\"))\n",
    "infections.properties(width=850, height=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[737512 737513 737514 737515 737516 737517 737518 737519 737520 737521\n",
      " 737522 737523 737524 737525 737526 737527 737528 737529 737530 737531\n",
      " 737532 737533 737534 737535 737536 737537 737538 737539 737540 737541]\n",
      "[ 57695  62435  66885  71808  77981  84794  91159  96092 100123 103375\n",
      " 107663 113296 118235 122171 125452 127854 130072 132210 134753 137698\n",
      " 141397 143724 145742 147065 148453 150648 153129 154999 156513 157770]\n"
     ]
    }
   ],
   "source": [
    "first_day_with_quarantine_effect = dt.datetime(year=2020, month=3, day=28)\n",
    "cases_since_quarantine = np.array(\n",
    "    data[data[\"day\"] >= first_day_with_quarantine_effect][\"cases\"])\n",
    "day_no_since_quarantine = np.array(\n",
    "    [d.toordinal() for d in data[data[\"day\"] >= first_day_with_quarantine_effect][\"day\"]])\n",
    "print(day_no_since_quarantine)\n",
    "print(cases_since_quarantine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mins: [0, 737512, 1e-12, 157770]\n",
      "maxs: [157770, 737942, 100.0, 83500000.0]\n",
      "[5.76889717e+04 7.37512086e+05 2.65712934e-09 2.02720125e+07]\n"
     ]
    }
   ],
   "source": [
    "def corona_curve(x, b0, x0, k, s):\n",
    "    return s * 1 / (1 + np.exp(-1 * k * s * (x - x0)) * (s / b0 - 1))\n",
    "\n",
    "print(\"mins:\", [0, day_no_since_quarantine[0], 1e-12, cases_since_quarantine[-1]])\n",
    "print(\"maxs:\", [cases_since_quarantine[-1], dt.datetime(year=2021, month=6, day=1).toordinal(), 1e2, 8.35e7])\n",
    "\n",
    "params, _ = curve_fit(\n",
    "    corona_curve, \n",
    "    xdata=day_no_since_quarantine, \n",
    "    ydata=cases_since_quarantine, \n",
    "    p0=[cases_since_quarantine[0], dt.datetime(year=2020, month=3, day=28).toordinal(), 8e-9, 5.6e7],\n",
    "    bounds=(\n",
    "        [0, day_no_since_quarantine[0], 1e-11, cases_since_quarantine[-1]], \n",
    "        [cases_since_quarantine[-1], dt.datetime(year=2021, month=6, day=1).toordinal(), 1e-8, 8.35e7])\n",
    ")\n",
    "print(params)"
   ]
  },
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   "execution_count": 5,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>curve</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>13440.544414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>14183.857308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>14968.247840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>15795.982629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>16669.452969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>372429.911440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>392641.902447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>413927.993344</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>436342.726866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>459943.133927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>67 rows × 2 columns</p>\n",
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      "text/plain": [
       "          day          curve\n",
       "0  2020-03-01   13440.544414\n",
       "1  2020-03-02   14183.857308\n",
       "2  2020-03-03   14968.247840\n",
       "3  2020-03-04   15795.982629\n",
       "4  2020-03-05   16669.452969\n",
       "..        ...            ...\n",
       "62 2020-05-02  372429.911440\n",
       "63 2020-05-03  392641.902447\n",
       "64 2020-05-04  413927.993344\n",
       "65 2020-05-05  436342.726866\n",
       "66 2020-05-06  459943.133927\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
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   "source": [
    "data = data[data[\"day\"] >= dt.datetime(year=2020, month=3, day=1)]\n",
    "projection_data = data.copy()\n",
    "projection_data = projection_data.drop([\"cases\"], axis=1)\n",
    "projection_data[\"curve\"] = [corona_curve(d.toordinal(), *params) for d in projection_data[\"day\"]]\n",
    "for i in range(10):\n",
    "    projection_data = projection_data.append(pd.DataFrame(\n",
    "        [[list(projection_data[\"day\"])[-1] + dt.timedelta(days=1), \n",
    "          corona_curve((list(projection_data[\"day\"])[-1] + dt.timedelta(days=1)).toordinal(), *params)]],\n",
    "        columns=(\"day\", \"curve\")\n",
    "    ), ignore_index=True)\n",
    "projection_data"
   ]
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      "text/plain": [
       "alt.LayerChart(...)"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "infections_chart = alt.Chart(data).mark_line(point=True, color=\"red\").encode(\n",
    "    alt.X(\"monthdate(day):O\", title=\"Tag\"),\n",
    "    alt.Y(\"cases:Q\", title=\"Corona-Fallzahlen\"))\n",
    "projection_chart = alt.Chart(projection_data).mark_line(point=False).encode(\n",
    "    alt.X(\"monthdate(day):O\", title=\"Tag\"),\n",
    "    alt.Y(\"curve:Q\", title=\"Fallzahlen (Projektion)\"))\n",
    "(projection_chart + infections_chart).properties(width=600, height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['318430', '159215', '106143']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bev_de = 83019213\n",
    "infizierte_pro_tag = [\"{:.0f}\".format(bev_de*0.7/(365*dauer)) for dauer in [0.5, 1, 1.5]]\n",
    "infizierte_pro_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-d7f82ceca037>:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  c1 = np.array(data[(md1 <= data[\"day\"])][(data[\"day\"] <= md2)][\"cases\"])\n",
      "<ipython-input-8-d7f82ceca037>:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  d1 = np.array([d.toordinal() for d in data[md1 <= data[\"day\"]][data[\"day\"] <= md2][\"day\"]])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.82759729e+02 7.37485098e+05 7.94076382e-09 3.12653509e+07] \n",
      " [2.58995320e+04 7.37506189e+05 4.71178611e-09 2.93265889e+07] \n",
      " [5.76889717e+04 7.37512086e+05 2.65712934e-09 2.02720125e+07]\n"
     ]
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    {
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\"2020-04-24T00:00:00\", \"Infektionszahlen\": 243612.41592474474, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-25T00:00:00\", \"Infektionszahlen\": 256923.6663295808, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-26T00:00:00\", \"Infektionszahlen\": 270952.41832770436, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-27T00:00:00\", \"Infektionszahlen\": 285736.24391013477, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-28T00:00:00\", \"Infektionszahlen\": 301314.55911779555, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-29T00:00:00\", \"Infektionszahlen\": 317728.7006526573, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-04-30T00:00:00\", \"Infektionszahlen\": 335022.0040575759, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-05-01T00:00:00\", \"Infektionszahlen\": null, \"Typ\": \"Mit Kontaktverbot\"}, {\"Tag\": \"2020-03-01T00:00:00\", \"Infektionszahlen\": 130.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-02T00:00:00\", \"Infektionszahlen\": 165.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-03T00:00:00\", \"Infektionszahlen\": 203.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-04T00:00:00\", \"Infektionszahlen\": 262.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-05T00:00:00\", \"Infektionszahlen\": 545.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-06T00:00:00\", \"Infektionszahlen\": 670.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-07T00:00:00\", \"Infektionszahlen\": 800.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-08T00:00:00\", \"Infektionszahlen\": 1040.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-09T00:00:00\", \"Infektionszahlen\": 1224.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-10T00:00:00\", \"Infektionszahlen\": 1565.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-11T00:00:00\", \"Infektionszahlen\": 1966.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-12T00:00:00\", \"Infektionszahlen\": 2745.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-03-13T00:00:00\", \"Infektionszahlen\": 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\"2020-04-04T00:00:00\", \"Infektionszahlen\": 96092.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-05T00:00:00\", \"Infektionszahlen\": 100123.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-06T00:00:00\", \"Infektionszahlen\": 103375.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-07T00:00:00\", \"Infektionszahlen\": 107663.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-08T00:00:00\", \"Infektionszahlen\": 113296.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-09T00:00:00\", \"Infektionszahlen\": 118235.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-10T00:00:00\", \"Infektionszahlen\": 122171.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-11T00:00:00\", \"Infektionszahlen\": 125452.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-12T00:00:00\", \"Infektionszahlen\": 127854.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-13T00:00:00\", \"Infektionszahlen\": 130072.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-14T00:00:00\", \"Infektionszahlen\": 132210.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-15T00:00:00\", \"Infektionszahlen\": 134753.0, \"Typ\": \"Fallzahlen\"}, {\"Tag\": \"2020-04-16T00:00:00\", \"Infektionszahlen\": 137698.0, \"Typ\": \"Fallzahlen\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"corona_infections.csv\", header=0, names=[\"day\", \"cases\"])\n",
    "data[\"day\"] = [dt.datetime.strptime(d + ' 2020', \"%b %d %Y\") for d in data[\"day\"]]\n",
    "md1 = dt.datetime(year=2020, month=3, day=20)\n",
    "md2 = dt.datetime(year=2020, month=3, day=28)\n",
    "c0 = np.array(data[data[\"day\"] <= md1][\"cases\"])\n",
    "d0 = np.array([d.toordinal() for d in data[data[\"day\"] <= md1][\"day\"]])\n",
    "c1 = np.array(data[(md1 <= data[\"day\"])][(data[\"day\"] <= md2)][\"cases\"])\n",
    "d1 = np.array([d.toordinal() for d in data[md1 <= data[\"day\"]][data[\"day\"] <= md2][\"day\"]])\n",
    "c2 = np.array(data[md2 <= data[\"day\"]][\"cases\"])\n",
    "d2 = np.array([d.toordinal() for d in data[md2 <= data[\"day\"]][\"day\"]])\n",
    "par0, _ = curve_fit(\n",
    "    corona_curve, \n",
    "    xdata=d0, \n",
    "    ydata=c0, \n",
    "    p0=[c0[0], dt.datetime(year=2020, month=3, day=1).toordinal(), 8e-9, 5.6e7],\n",
    "    bounds=(\n",
    "        [0, d0[0], 1e-11, c0[-1]], \n",
    "        [c0[-1], dt.datetime(year=2021, month=6, day=1).toordinal(), 1e-8, 8.35e7])\n",
    ")\n",
    "par1, _ = curve_fit(\n",
    "    corona_curve, \n",
    "    xdata=d1, \n",
    "    ydata=c1, \n",
    "    p0=[c1[0], dt.datetime(year=2020, month=3, day=20).toordinal(), 8e-9, 5.6e7],\n",
    "    bounds=(\n",
    "        [0, d1[0], 1e-11, c1[-1]], \n",
    "        [c1[-1], dt.datetime(year=2021, month=6, day=1).toordinal(), 1e-8, 8.35e7])\n",
    ")\n",
    "par2, _ = curve_fit(\n",
    "    corona_curve, \n",
    "    xdata=d2, \n",
    "    ydata=c2, \n",
    "    p0=[c2[0], dt.datetime(year=2020, month=3, day=28).toordinal(), 8e-9, 5.6e7],\n",
    "    bounds=(\n",
    "        [0, d2[0], 1e-11, c2[-1]], \n",
    "        [c2[-1], dt.datetime(year=2021, month=6, day=1).toordinal(), 1e-8, 8.35e7])\n",
    ")\n",
    "print(par0, \"\\n\", par1, \"\\n\", par2)\n",
    "data = data[data[\"day\"] >= dt.datetime(year=2020, month=3, day=1)]\n",
    "data = data.rename(columns={\"day\": \"Tag\", \"cases\": \"Infektionszahlen\"})\n",
    "data[\"Typ\"] = [\"Fallzahlen\" for d in data[\"Tag\"]]\n",
    "data[\"Typ\"] = data[\"Typ\"].astype(str)\n",
    "data_count = len(data) + 5\n",
    "for par, t in [(par0, \"Ungebremste Ausbreitung\"), \n",
    "               (par1, \"Mit Ausgangsbeschränkungen\"), \n",
    "               (par2, \"Mit Kontaktverbot\")]:\n",
    "    for i in range(data_count):\n",
    "        curve_value = corona_curve((list(data[\"Tag\"])[0] + dt.timedelta(days=i)).toordinal(), *par)\n",
    "        data = data.append(pd.DataFrame(\n",
    "            [[list(data[\"Tag\"])[0] + dt.timedelta(days=i), \n",
    "              curve_value if curve_value < 350000 else None,\n",
    "              t]],\n",
    "            columns=(\"Tag\", \"Infektionszahlen\", \"Typ\")\n",
    "        ), ignore_index=True)\n",
    "data = data.tail(len(data)-data_count+15).append(data.head(data_count-15), ignore_index=True)\n",
    "line = alt.Chart(data).mark_line(point=True).encode(\n",
    "    alt.X(\"monthdate(Tag)\", title=\"Tag\"),\n",
    "    alt.Y(\"Infektionszahlen:Q\", title=\"Infektionszahlen\"),\n",
    "    color=alt.Color(\"Typ:N\", \n",
    "                    scale=alt.Scale(scheme=\"set1\"),\n",
    "                    legend=alt.Legend(\n",
    "        orient=\"none\", legendX=20, legendY=20,\n",
    "        fillColor=\"white\", strokeColor=\"black\", cornerRadius=7, padding=6,\n",
    "        title=\"Prognosen nach Maßnahmen\"))\n",
    ")\n",
    "nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
    "                        fields=['Tag'], empty='none')\n",
    "selectors = alt.Chart(data).mark_point().encode(\n",
    "    alt.X(\"monthdate(Tag)\", title=\"Tag\"),\n",
    "    opacity=alt.value(0),\n",
    ").add_selection(nearest)\n",
    "points = line.mark_point().encode(\n",
    "    opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
    ")\n",
    "text = line.mark_text(align='left', dx=5, dy=5).encode(\n",
    "    text=alt.condition(nearest, 'Infektionszahlen:Q', alt.value(' '))\n",
    ")\n",
    "rules = alt.Chart(data).mark_rule(color='gray').encode(\n",
    "    alt.X(\"monthdate(Tag)\", title=\"Tag\"),\n",
    ").transform_filter(nearest)\n",
    "alt.layer(\n",
    "    line, selectors, points, rules, text\n",
    ").properties(width=850, height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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   "codemirror_mode": {
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